{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2023-11-01T02:23:59.409426800Z",
     "start_time": "2023-11-01T02:23:11.764813300Z"
    }
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "import torchvision\n",
    "import torch.nn as nn\n",
    "from torchvision import datasets,transforms\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [
    {
     "data": {
      "text/plain": "True"
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.cuda.is_available()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-01T02:24:05.964743400Z",
     "start_time": "2023-11-01T02:24:04.973239800Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 准备数据集"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [],
   "source": [
    "data_transform = {\n",
    "    'train':transforms.Compose([\n",
    "        transforms.Resize(230),\n",
    "        transforms.CenterCrop(224),\n",
    "        transforms.RandomHorizontalFlip(),\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize((0.5,0.5,0.5),(0.5,0.5,0.5))\n",
    "    ]),\n",
    "    'test':transforms.Compose([\n",
    "        transforms.Resize(230),\n",
    "        transforms.CenterCrop(224),\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize((0.5,0.5,0.5),(0.5,0.5,0.5))\n",
    "    ])\n",
    "}\n",
    "import os\n",
    "data_path=\"F:/机器学习数据集/代码资源_PyTorch深度学习入门/CH4/data\"\n",
    "trainset = datasets.ImageFolder(os.path.join(data_path,'train'),transform=data_transform['train'])\n",
    "testset = datasets.ImageFolder(os.path.join(data_path,'test'),transform=data_transform['test'])\n",
    "validset = datasets.ImageFolder(os.path.join(data_path,'valid'),transform=data_transform['test'])\n",
    "train_loader = torch.utils.data.DataLoader(trainset,batch_size=5,shuffle=True)\n",
    "test_loader = torch.utils.data.DataLoader(testset,batch_size=5,shuffle=True)\n",
    "valid_loader = torch.utils.data.DataLoader(validset,batch_size=5,shuffle=True)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-01T02:35:48.609985900Z",
     "start_time": "2023-11-01T02:35:48.223808900Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "羊驼\n",
      "羊驼\n",
      "熊猫\n",
      "羊驼\n",
      "熊猫\n"
     ]
    }
   ],
   "source": [
    "def imshow(inputs):\n",
    "    inputs = inputs/2+0.5\n",
    "    inputs = inputs.numpy().transpose(1,2,0)\n",
    "    plt.imshow(inputs)\n",
    "    plt.show()\n",
    "\n",
    "imgs,labels = next(iter(valid_loader))\n",
    "imshow(torchvision.utils.make_grid(imgs))\n",
    "class_names = ['羊驼','熊猫']\n",
    "for i in labels:\n",
    "    print(class_names[i])"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-01T02:38:41.413212900Z",
     "start_time": "2023-11-01T02:38:37.968645100Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 准备训练函数和测试函数"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [],
   "source": [
    "def train_loop(model,loss_fn,optimizer,n_epochs,train_loader):\n",
    "    for epoch in range(n_epochs):\n",
    "        loss_train = 0.0\n",
    "        for i,data in enumerate(train_loader,0):\n",
    "            imgs,labels = data\n",
    "            imgs,labels = imgs.cuda(),labels.cuda()\n",
    "            outputs = model(imgs)\n",
    "            loss = loss_fn(outputs,labels)\n",
    "            optimizer.zero_grad()\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "            loss_train+=loss.item()\n",
    "            if i%10==9:\n",
    "                print(\"Epoch:{}, i:{}, 训练损失:{}\".format(epoch+1,i+1,loss_train/len(train_loader)))\n",
    "                loss_train=0.0"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-01T02:42:15.969110Z",
     "start_time": "2023-11-01T02:42:15.919115100Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [],
   "source": [
    "# 测试函数\n",
    "def test_loop(model,test_loader):\n",
    "    correct = 0\n",
    "    total = 0\n",
    "    with torch.no_grad():\n",
    "        for data in test_loader:\n",
    "            imgs,labels = data\n",
    "            imgs,labels = imgs.cuda(),labels.cuda()\n",
    "            outputs = model(imgs)\n",
    "            _,preds = torch.max(outputs,dim=1)\n",
    "            total+=labels.shape[0]\n",
    "            correct+=int((labels==preds).sum())\n",
    "    print('测试集精度:{:.3f}%'.format(correct/total*100))"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-01T02:43:27.680664300Z",
     "start_time": "2023-11-01T02:43:27.653668900Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Alexnet网络"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\myAnaconda\\envs\\PyTorch\\lib\\site-packages\\torchvision\\models\\_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
      "  warnings.warn(\n",
      "E:\\myAnaconda\\envs\\PyTorch\\lib\\site-packages\\torchvision\\models\\_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=AlexNet_Weights.IMAGENET1K_V1`. You can also use `weights=AlexNet_Weights.DEFAULT` to get the most up-to-date weights.\n",
      "  warnings.warn(msg)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AlexNet(\n",
      "  (features): Sequential(\n",
      "    (0): Conv2d(3, 64, kernel_size=(11, 11), stride=(4, 4), padding=(2, 2))\n",
      "    (1): ReLU(inplace=True)\n",
      "    (2): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "    (3): Conv2d(64, 192, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))\n",
      "    (4): ReLU(inplace=True)\n",
      "    (5): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "    (6): Conv2d(192, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (7): ReLU(inplace=True)\n",
      "    (8): Conv2d(384, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (9): ReLU(inplace=True)\n",
      "    (10): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (11): ReLU(inplace=True)\n",
      "    (12): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "  )\n",
      "  (avgpool): AdaptiveAvgPool2d(output_size=(6, 6))\n",
      "  (classifier): Sequential(\n",
      "    (0): Dropout(p=0.5, inplace=False)\n",
      "    (1): Linear(in_features=9216, out_features=4096, bias=True)\n",
      "    (2): ReLU(inplace=True)\n",
      "    (3): Dropout(p=0.5, inplace=False)\n",
      "    (4): Linear(in_features=4096, out_features=4096, bias=True)\n",
      "    (5): ReLU(inplace=True)\n",
      "    (6): Linear(in_features=4096, out_features=1000, bias=True)\n",
      "  )\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "alexnet = torchvision.models.alexnet(pretrained=True)\n",
    "print(alexnet)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-01T02:44:56.647347400Z",
     "start_time": "2023-11-01T02:44:55.253049200Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [],
   "source": [
    "# 微调模型\n",
    "for p in alexnet.parameters():\n",
    "    p.requires_grad=False # 关闭原来模型的梯度更新\n",
    "\n",
    "alexnet.classifier = nn.Sequential(\n",
    "    nn.Dropout(p=0.5,inplace=False),\n",
    "    nn.Linear(9216,4096,bias=True),\n",
    "    nn.ReLU(inplace=True),\n",
    "    nn.Dropout(p=0.5,inplace=False),\n",
    "    nn.Linear(4096,4096,bias=True),\n",
    "    nn.ReLU(inplace=True),\n",
    "    nn.Linear(4096,2)\n",
    ")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-01T02:46:39.031308800Z",
     "start_time": "2023-11-01T02:46:38.620305300Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:1, i:10, 训练损失:0.08998439013957978\n",
      "Epoch:1, i:20, 训练损失:0.05977212749421597\n",
      "Epoch:1, i:30, 训练损失:0.03238944867625833\n",
      "Epoch:1, i:40, 训练损失:0.016416732221841812\n",
      "Epoch:1, i:50, 训练损失:0.021246026107110082\n",
      "Epoch:1, i:60, 训练损失:0.007942388579249383\n",
      "Epoch:1, i:70, 训练损失:0.01003171522897901\n",
      "Epoch:1, i:80, 训练损失:0.029540006129536778\n",
      "Epoch:2, i:10, 训练损失:0.014803287219547202\n",
      "Epoch:2, i:20, 训练损失:0.017793383541720685\n",
      "Epoch:2, i:30, 训练损失:0.017789138807711425\n",
      "Epoch:2, i:40, 训练损失:0.019388319668496478\n",
      "Epoch:2, i:50, 训练损失:0.026474987407709705\n",
      "Epoch:2, i:60, 训练损失:0.004740491036318417\n",
      "Epoch:2, i:70, 训练损失:0.011078076626290567\n",
      "Epoch:2, i:80, 训练损失:0.0064474468971638995\n"
     ]
    }
   ],
   "source": [
    "import torch.optim as optim\n",
    "optimizer = optim.SGD(alexnet.parameters(),lr=0.001,momentum=0.9)\n",
    "alexnetcuda = alexnet.cuda()\n",
    "loss_fn = nn.CrossEntropyLoss()\n",
    "train_loop(model=alexnetcuda,optimizer=optimizer,loss_fn=loss_fn,n_epochs=2,train_loader=train_loader)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-01T02:50:29.063398900Z",
     "start_time": "2023-11-01T02:49:45.985241700Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "测试集精度:96.117%\n"
     ]
    }
   ],
   "source": [
    "test_loop(alexnetcuda.eval(),test_loader)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-01T02:51:07.393698500Z",
     "start_time": "2023-11-01T02:50:54.070644Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "测试集精度:96.117%\n"
     ]
    }
   ],
   "source": [
    "test_loop(alexnetcuda.eval(),test_loader=valid_loader)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-01T02:51:32.048769300Z",
     "start_time": "2023-11-01T02:51:21.266153900Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## vgg模型"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\myAnaconda\\envs\\PyTorch\\lib\\site-packages\\torchvision\\models\\_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
      "  warnings.warn(\n",
      "E:\\myAnaconda\\envs\\PyTorch\\lib\\site-packages\\torchvision\\models\\_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights.\n",
      "  warnings.warn(msg)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "VGG(\n",
      "  (features): Sequential(\n",
      "    (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (1): ReLU(inplace=True)\n",
      "    (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (3): ReLU(inplace=True)\n",
      "    (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "    (5): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (6): ReLU(inplace=True)\n",
      "    (7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (8): ReLU(inplace=True)\n",
      "    (9): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "    (10): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (11): ReLU(inplace=True)\n",
      "    (12): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (13): ReLU(inplace=True)\n",
      "    (14): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (15): ReLU(inplace=True)\n",
      "    (16): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "    (17): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (18): ReLU(inplace=True)\n",
      "    (19): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (20): ReLU(inplace=True)\n",
      "    (21): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (22): ReLU(inplace=True)\n",
      "    (23): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "    (24): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (25): ReLU(inplace=True)\n",
      "    (26): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (27): ReLU(inplace=True)\n",
      "    (28): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (29): ReLU(inplace=True)\n",
      "    (30): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "  )\n",
      "  (avgpool): AdaptiveAvgPool2d(output_size=(7, 7))\n",
      "  (classifier): Sequential(\n",
      "    (0): Linear(in_features=25088, out_features=4096, bias=True)\n",
      "    (1): ReLU(inplace=True)\n",
      "    (2): Dropout(p=0.5, inplace=False)\n",
      "    (3): Linear(in_features=4096, out_features=4096, bias=True)\n",
      "    (4): ReLU(inplace=True)\n",
      "    (5): Dropout(p=0.5, inplace=False)\n",
      "    (6): Linear(in_features=4096, out_features=1000, bias=True)\n",
      "  )\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "vgg = torchvision.models.vgg16(pretrained=True)\n",
    "print(vgg)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-01T02:53:06.965351800Z",
     "start_time": "2023-11-01T02:53:04.349368600Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "outputs": [],
   "source": [
    "for p in vgg.parameters():\n",
    "    p.requires_grad=False\n",
    "\n",
    "vgg.classifier = nn.Sequential(\n",
    "    nn.Linear(25088,4096,bias=True),\n",
    "    nn.ReLU(inplace=True),\n",
    "    nn.Dropout(p=0.5,inplace=False),\n",
    "    nn.Linear(4096,4096,bias=True),\n",
    "    nn.ReLU(inplace=True),\n",
    "    nn.Dropout(p=0.5,inplace=False),\n",
    "    nn.Linear(4096,2,bias=True)\n",
    ")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-01T02:53:42.692007Z",
     "start_time": "2023-11-01T02:53:41.600950Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:1, i:10, 训练损失:0.07532677352428437\n",
      "Epoch:1, i:20, 训练损失:0.036319248378276825\n",
      "Epoch:1, i:30, 训练损失:0.021748395659960805\n",
      "Epoch:1, i:40, 训练损失:0.012905919435434043\n",
      "Epoch:1, i:50, 训练损失:0.009030132276529912\n",
      "Epoch:1, i:60, 训练损失:0.004975737644417677\n",
      "Epoch:1, i:70, 训练损失:0.004352021950762719\n",
      "Epoch:1, i:80, 训练损失:0.002991667959213373\n",
      "Epoch:2, i:10, 训练损失:0.0005162371387996245\n",
      "Epoch:2, i:20, 训练损失:0.0019138384805046371\n",
      "Epoch:2, i:30, 训练损失:0.0006065570536520681\n",
      "Epoch:2, i:40, 训练损失:0.0009657644961407641\n",
      "Epoch:2, i:50, 训练损失:0.00032146424482562\n",
      "Epoch:2, i:60, 训练损失:0.00041265058512180987\n",
      "Epoch:2, i:70, 训练损失:0.00036344505360830226\n",
      "Epoch:2, i:80, 训练损失:0.0007464689035259653\n"
     ]
    }
   ],
   "source": [
    "optimizer = optim.SGD(vgg.parameters(),lr=0.001,momentum=0.9)\n",
    "vggcuda=vgg.cuda()\n",
    "train_loop(model=vggcuda,loss_fn=loss_fn,optimizer=optimizer,train_loader=train_loader,n_epochs=2)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-01T02:57:02.210993400Z",
     "start_time": "2023-11-01T02:56:04.040085700Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "测试集精度:99.029%\n"
     ]
    }
   ],
   "source": [
    "test_loop(vggcuda.eval(),test_loader=test_loader)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-01T03:15:47.103152200Z",
     "start_time": "2023-11-01T03:15:33.927161Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "测试集精度:99.029%\n"
     ]
    }
   ],
   "source": [
    "test_loop(vggcuda.eval(),test_loader=valid_loader)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-01T03:15:59.842155700Z",
     "start_time": "2023-11-01T03:15:47.112155600Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## resnet模型"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\myAnaconda\\envs\\PyTorch\\lib\\site-packages\\torchvision\\models\\_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
      "  warnings.warn(\n",
      "E:\\myAnaconda\\envs\\PyTorch\\lib\\site-packages\\torchvision\\models\\_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNet101_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet101_Weights.DEFAULT` to get the most up-to-date weights.\n",
      "  warnings.warn(msg)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ResNet(\n",
      "  (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
      "  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  (relu): ReLU(inplace=True)\n",
      "  (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
      "  (layer1): Sequential(\n",
      "    (0): Bottleneck(\n",
      "      (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "      (downsample): Sequential(\n",
      "        (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (1): Bottleneck(\n",
      "      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (2): Bottleneck(\n",
      "      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "  )\n",
      "  (layer2): Sequential(\n",
      "    (0): Bottleneck(\n",
      "      (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "      (downsample): Sequential(\n",
      "        (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
      "        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (1): Bottleneck(\n",
      "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (2): Bottleneck(\n",
      "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (3): Bottleneck(\n",
      "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "  )\n",
      "  (layer3): Sequential(\n",
      "    (0): Bottleneck(\n",
      "      (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "      (downsample): Sequential(\n",
      "        (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
      "        (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (1): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (2): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (3): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (4): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (5): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (6): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (7): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (8): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (9): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (10): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (11): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (12): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (13): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (14): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (15): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (16): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (17): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (18): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (19): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (20): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (21): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (22): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "  )\n",
      "  (layer4): Sequential(\n",
      "    (0): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "      (downsample): Sequential(\n",
      "        (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
      "        (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (1): Bottleneck(\n",
      "      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (2): Bottleneck(\n",
      "      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "  )\n",
      "  (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n",
      "  (fc): Linear(in_features=2048, out_features=1000, bias=True)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "resnet = torchvision.models.resnet101(pretrained=True)\n",
    "print(resnet)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-01T03:07:46.127412100Z",
     "start_time": "2023-11-01T03:07:43.939881500Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "outputs": [],
   "source": [
    "for p in resnet.parameters():\n",
    "    p.requires_grad=False\n",
    "resnet.fc = nn.Linear(2048,2)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-01T03:12:43.827065400Z",
     "start_time": "2023-11-01T03:12:43.781054900Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:1, i:10, 训练损失:0.0836891632527113\n",
      "Epoch:1, i:20, 训练损失:0.07773860152810812\n",
      "Epoch:1, i:30, 训练损失:0.03486252026632428\n",
      "Epoch:1, i:40, 训练损失:0.04815451493486762\n",
      "Epoch:1, i:50, 训练损失:0.02906628684140742\n",
      "Epoch:1, i:60, 训练损失:0.016205617878586053\n",
      "Epoch:1, i:70, 训练损失:0.014501524367369711\n",
      "Epoch:1, i:80, 训练损失:0.015884936251677573\n",
      "Epoch:2, i:10, 训练损失:0.02956674372544512\n",
      "Epoch:2, i:20, 训练损失:0.02944566698279232\n",
      "Epoch:2, i:30, 训练损失:0.023656456754542886\n",
      "Epoch:2, i:40, 训练损失:0.028597357880789785\n",
      "Epoch:2, i:50, 训练损失:0.03573724832385779\n",
      "Epoch:2, i:60, 训练损失:0.036888812470715494\n",
      "Epoch:2, i:70, 训练损失:0.024829810473602266\n",
      "Epoch:2, i:80, 训练损失:0.052383179869502784\n"
     ]
    }
   ],
   "source": [
    "optimizer= optim.SGD(resnet.parameters(),lr=0.001,momentum=0.9)\n",
    "\n",
    "train_loop(model=resnet.cuda(),loss_fn=loss_fn,n_epochs=2,optimizer=optimizer,train_loader=train_loader)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-01T03:17:16.191744600Z",
     "start_time": "2023-11-01T03:16:26.301109400Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "测试集精度:94.660%\n"
     ]
    }
   ],
   "source": [
    "test_loop(resnet.cuda().eval(),test_loader=test_loader)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-01T03:18:15.977191100Z",
     "start_time": "2023-11-01T03:18:03.786187700Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "测试集精度:94.660%\n"
     ]
    }
   ],
   "source": [
    "test_loop(resnet.cuda().eval(),test_loader=valid_loader)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-01T03:18:29.650510800Z",
     "start_time": "2023-11-01T03:18:19.095509400Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## squeezenet"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SqueezeNet(\n",
      "  (features): Sequential(\n",
      "    (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(2, 2))\n",
      "    (1): ReLU(inplace=True)\n",
      "    (2): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=True)\n",
      "    (3): Fire(\n",
      "      (squeeze): Conv2d(64, 16, kernel_size=(1, 1), stride=(1, 1))\n",
      "      (squeeze_activation): ReLU(inplace=True)\n",
      "      (expand1x1): Conv2d(16, 64, kernel_size=(1, 1), stride=(1, 1))\n",
      "      (expand1x1_activation): ReLU(inplace=True)\n",
      "      (expand3x3): Conv2d(16, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "      (expand3x3_activation): ReLU(inplace=True)\n",
      "    )\n",
      "    (4): Fire(\n",
      "      (squeeze): Conv2d(128, 16, kernel_size=(1, 1), stride=(1, 1))\n",
      "      (squeeze_activation): ReLU(inplace=True)\n",
      "      (expand1x1): Conv2d(16, 64, kernel_size=(1, 1), stride=(1, 1))\n",
      "      (expand1x1_activation): ReLU(inplace=True)\n",
      "      (expand3x3): Conv2d(16, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "      (expand3x3_activation): ReLU(inplace=True)\n",
      "    )\n",
      "    (5): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=True)\n",
      "    (6): Fire(\n",
      "      (squeeze): Conv2d(128, 32, kernel_size=(1, 1), stride=(1, 1))\n",
      "      (squeeze_activation): ReLU(inplace=True)\n",
      "      (expand1x1): Conv2d(32, 128, kernel_size=(1, 1), stride=(1, 1))\n",
      "      (expand1x1_activation): ReLU(inplace=True)\n",
      "      (expand3x3): Conv2d(32, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "      (expand3x3_activation): ReLU(inplace=True)\n",
      "    )\n",
      "    (7): Fire(\n",
      "      (squeeze): Conv2d(256, 32, kernel_size=(1, 1), stride=(1, 1))\n",
      "      (squeeze_activation): ReLU(inplace=True)\n",
      "      (expand1x1): Conv2d(32, 128, kernel_size=(1, 1), stride=(1, 1))\n",
      "      (expand1x1_activation): ReLU(inplace=True)\n",
      "      (expand3x3): Conv2d(32, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "      (expand3x3_activation): ReLU(inplace=True)\n",
      "    )\n",
      "    (8): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=True)\n",
      "    (9): Fire(\n",
      "      (squeeze): Conv2d(256, 48, kernel_size=(1, 1), stride=(1, 1))\n",
      "      (squeeze_activation): ReLU(inplace=True)\n",
      "      (expand1x1): Conv2d(48, 192, kernel_size=(1, 1), stride=(1, 1))\n",
      "      (expand1x1_activation): ReLU(inplace=True)\n",
      "      (expand3x3): Conv2d(48, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "      (expand3x3_activation): ReLU(inplace=True)\n",
      "    )\n",
      "    (10): Fire(\n",
      "      (squeeze): Conv2d(384, 48, kernel_size=(1, 1), stride=(1, 1))\n",
      "      (squeeze_activation): ReLU(inplace=True)\n",
      "      (expand1x1): Conv2d(48, 192, kernel_size=(1, 1), stride=(1, 1))\n",
      "      (expand1x1_activation): ReLU(inplace=True)\n",
      "      (expand3x3): Conv2d(48, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "      (expand3x3_activation): ReLU(inplace=True)\n",
      "    )\n",
      "    (11): Fire(\n",
      "      (squeeze): Conv2d(384, 64, kernel_size=(1, 1), stride=(1, 1))\n",
      "      (squeeze_activation): ReLU(inplace=True)\n",
      "      (expand1x1): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1))\n",
      "      (expand1x1_activation): ReLU(inplace=True)\n",
      "      (expand3x3): Conv2d(64, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "      (expand3x3_activation): ReLU(inplace=True)\n",
      "    )\n",
      "    (12): Fire(\n",
      "      (squeeze): Conv2d(512, 64, kernel_size=(1, 1), stride=(1, 1))\n",
      "      (squeeze_activation): ReLU(inplace=True)\n",
      "      (expand1x1): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1))\n",
      "      (expand1x1_activation): ReLU(inplace=True)\n",
      "      (expand3x3): Conv2d(64, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "      (expand3x3_activation): ReLU(inplace=True)\n",
      "    )\n",
      "  )\n",
      "  (classifier): Sequential(\n",
      "    (0): Dropout(p=0.5, inplace=False)\n",
      "    (1): Conv2d(512, 1000, kernel_size=(1, 1), stride=(1, 1))\n",
      "    (2): ReLU(inplace=True)\n",
      "    (3): AdaptiveAvgPool2d(output_size=(1, 1))\n",
      "  )\n",
      ")\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\myAnaconda\\envs\\PyTorch\\lib\\site-packages\\torchvision\\models\\_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
      "  warnings.warn(\n",
      "E:\\myAnaconda\\envs\\PyTorch\\lib\\site-packages\\torchvision\\models\\_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=SqueezeNet1_1_Weights.IMAGENET1K_V1`. You can also use `weights=SqueezeNet1_1_Weights.DEFAULT` to get the most up-to-date weights.\n",
      "  warnings.warn(msg)\n"
     ]
    }
   ],
   "source": [
    "squeezenet = torchvision.models.squeezenet1_1(pretrained=True)\n",
    "print(squeezenet)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-01T04:38:40.696889100Z",
     "start_time": "2023-11-01T04:38:40.640906800Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "outputs": [],
   "source": [
    "for p in squeezenet.parameters():\n",
    "    p.requires_grad=False\n",
    "\n",
    "squeezenet.classifier = nn.Sequential(\n",
    "    nn.Dropout(p=0.5,inplace=False),\n",
    "    nn.Conv2d(512,2,kernel_size=(1,1),stride=(1,1)),\n",
    "    nn.ReLU(inplace=True),\n",
    "    nn.AdaptiveAvgPool2d(output_size=(1,1))\n",
    ")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-01T04:38:45.053172500Z",
     "start_time": "2023-11-01T04:38:45.035810400Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:1, i:10, 训练损失:0.07472046297043562\n",
      "Epoch:1, i:20, 训练损失:0.02829014357412234\n",
      "Epoch:1, i:30, 训练损失:0.008879823092138394\n",
      "Epoch:1, i:40, 训练损失:0.01526187212966761\n",
      "Epoch:1, i:50, 训练损失:0.018745702435262503\n",
      "Epoch:1, i:60, 训练损失:0.003032125122626894\n",
      "Epoch:1, i:70, 训练损失:0.01998856383902421\n",
      "Epoch:1, i:80, 训练损失:0.017312925229634856\n",
      "Epoch:2, i:10, 训练损失:0.001664963859184354\n",
      "Epoch:2, i:20, 训练损失:0.0010578575638646726\n",
      "Epoch:2, i:30, 训练损失:0.004725730414429563\n",
      "Epoch:2, i:40, 训练损失:0.0008016109608433908\n",
      "Epoch:2, i:50, 训练损失:0.00917275816163965\n",
      "Epoch:2, i:60, 训练损失:0.012069177838566248\n",
      "Epoch:2, i:70, 训练损失:0.0036917028804964503\n",
      "Epoch:2, i:80, 训练损失:0.0029011596321652177\n"
     ]
    }
   ],
   "source": [
    "optimizer = optim.SGD(squeezenet.parameters(),lr=0.001,momentum=0.9)\n",
    "train_loop(model=squeezenet.cuda(),loss_fn=loss_fn,n_epochs=2,train_loader=train_loader,optimizer=optimizer)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-01T04:39:22.535329200Z",
     "start_time": "2023-11-01T04:38:45.881712900Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "测试集精度:98.058%\n"
     ]
    }
   ],
   "source": [
    "test_loop(squeezenet.cuda().eval(),test_loader=test_loader)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-01T04:40:03.573489600Z",
     "start_time": "2023-11-01T04:39:56.375488400Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "测试集精度:98.058%\n"
     ]
    }
   ],
   "source": [
    "test_loop(squeezenet.cuda().eval(),test_loader=test_loader)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-01T04:40:25.355991900Z",
     "start_time": "2023-11-01T04:40:18.514994300Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "outputs": [],
   "source": [
    "torch.save(vggcuda.eval(),'vgg_gud_panda.pkl')"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-01T04:44:26.065691200Z",
     "start_time": "2023-11-01T04:44:17.924843700Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "VGG(\n",
      "  (features): Sequential(\n",
      "    (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (1): ReLU(inplace=True)\n",
      "    (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (3): ReLU(inplace=True)\n",
      "    (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "    (5): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (6): ReLU(inplace=True)\n",
      "    (7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (8): ReLU(inplace=True)\n",
      "    (9): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "    (10): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (11): ReLU(inplace=True)\n",
      "    (12): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (13): ReLU(inplace=True)\n",
      "    (14): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (15): ReLU(inplace=True)\n",
      "    (16): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "    (17): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (18): ReLU(inplace=True)\n",
      "    (19): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (20): ReLU(inplace=True)\n",
      "    (21): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (22): ReLU(inplace=True)\n",
      "    (23): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "    (24): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (25): ReLU(inplace=True)\n",
      "    (26): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (27): ReLU(inplace=True)\n",
      "    (28): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (29): ReLU(inplace=True)\n",
      "    (30): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "  )\n",
      "  (avgpool): AdaptiveAvgPool2d(output_size=(7, 7))\n",
      "  (classifier): Sequential(\n",
      "    (0): Linear(in_features=25088, out_features=4096, bias=True)\n",
      "    (1): ReLU(inplace=True)\n",
      "    (2): Dropout(p=0.5, inplace=False)\n",
      "    (3): Linear(in_features=4096, out_features=4096, bias=True)\n",
      "    (4): ReLU(inplace=True)\n",
      "    (5): Dropout(p=0.5, inplace=False)\n",
      "    (6): Linear(in_features=4096, out_features=2, bias=True)\n",
      "  )\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "clf = torch.load('vgg_gud_panda.pkl')\n",
    "print(clf)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-01T04:45:09.055454700Z",
     "start_time": "2023-11-01T04:45:08.267454800Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "真实值---预测值\n",
      "羊驼 --- 羊驼\n",
      "羊驼 --- 羊驼\n",
      "熊猫 --- 熊猫\n",
      "羊驼 --- 羊驼\n",
      "熊猫 --- 熊猫\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "outputs = clf(imgs.cuda())\n",
    "_,preds = torch.max(outputs,dim=1)\n",
    "print('真实值---预测值')\n",
    "for i,j in zip(labels,preds):\n",
    "    print(class_names[i],'---',class_names[j])\n",
    "imshow(torchvision.utils.make_grid(imgs))"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-01T04:50:15.396585300Z",
     "start_time": "2023-11-01T04:50:11.044480300Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
